Lungs_X_ray_Classification.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Lungs X-ray Classification.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z9ssdcrQvn8f",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "80be9069-4bba-476e-91fa-ec3d6c92b4e3"
      },
      "source": [
        "%tensorflow_version 2.x\n",
        "import tensorflow as tf\n",
        "device_name = tf.test.gpu_device_name()\n",
        "if device_name != '/device:GPU:0':\n",
        "  raise SystemError('GPU device not found')\n",
        "print('Found GPU at: {}'.format(device_name))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found GPU at: /device:GPU:0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O51byyoD1-Kt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "b0793074-d5d0-4bd3-a6c9-f6840be5ced5"
      },
      "source": [
        "import tensorflow as tf\n",
        "import keras"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ENBoGNVe2DYw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from keras.layers import Conv2D, MaxPooling2D, Flatten, Dropout, Activation, AvgPool2D, MaxPool2D"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2a6998iZ2Oux",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os\n",
        "import random\n",
        "import cv2\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uet0uTm22Q8_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!pip install -U -q PyDrive\n",
        "from pydrive.auth import GoogleAuth\n",
        "from pydrive.drive import GoogleDrive\n",
        "from google.colab import auth\n",
        "from oauth2client.client import GoogleCredentials"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LsEEEOro2Tyu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "auth.authenticate_user()\n",
        "gauth = GoogleAuth()\n",
        "gauth.credentials = GoogleCredentials.get_application_default()\n",
        "drive = GoogleDrive(gauth)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b4s6-tIU2ccl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "fid = drive.ListFile({'q':\"title='project.zip'\"}).GetList()[0]['id']\n",
        "f = drive.CreateFile({'id': fid})\n",
        "f.GetContentFile('project.zip')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mh2MXYBV3O8s",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "8082134f-ef94-424c-f444-4ffdbc0853e1"
      },
      "source": [
        "f.keys()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['id', 'kind', 'etag', 'selfLink', 'webContentLink', 'alternateLink', 'embedLink', 'iconLink', 'title', 'mimeType', 'labels', 'copyRequiresWriterPermission', 'createdDate', 'modifiedDate', 'lastViewedByMeDate', 'markedViewedByMeDate', 'version', 'parents', 'downloadUrl', 'userPermission', 'originalFilename', 'fileExtension', 'md5Checksum', 'fileSize', 'quotaBytesUsed', 'ownerNames', 'owners', 'lastModifyingUserName', 'lastModifyingUser', 'capabilities', 'editable', 'copyable', 'writersCanShare', 'shared', 'explicitlyTrashed', 'appDataContents', 'headRevisionId', 'spaces'])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3rDU6XcGQKDv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "c1871c3f-0cf0-4ed7-ae5f-d5d93549f31a"
      },
      "source": [
        "!unzip project.zip"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1NZ0GKNVQW6h",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "PATH = '/content/Coronahack-Chest-XRay-Dataset/Coronahack-Chest-XRay-Dataset'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dd73TY-QUQgP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "2721ae71-a1fa-416d-814b-78d57bc5a837"
      },
      "source": [
        "os.listdir(PATH)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['test', 'train']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z3lX_P6nUVBO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_dir = os.path.join(PATH,'train')\n",
        "test_dir = os.path.join(PATH,'test')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f7vM29dyjvNi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "img_size = 100"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OtaQBIG7Uqyu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data = []\n",
        "for img in os.listdir(train_dir):\n",
        "  i = img\n",
        "  try:\n",
        "    img_array = cv2.imread(os.path.join(train_dir,img),cv2.IMREAD_GRAYSCALE)\n",
        "    new_array = cv2.resize(img_array,(img_size, img_size))\n",
        "    if \"virus\" in i:\n",
        "      class_name = 1\n",
        "      data.append([new_array,class_name])\n",
        "    elif \"bacteria\" in i:\n",
        "      class_name = 2\n",
        "      data.append([new_array,class_name])\n",
        "    elif \"NORMAL\" in i:\n",
        "      class_name = 0\n",
        "      data.append([new_array,class_name])  \n",
        "    else:\n",
        "      pass\n",
        "  except Exception as e:\n",
        "    pass"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4Tyk7364xeSM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data_test = []\n",
        "for img in os.listdir(test_dir):\n",
        "  i = img\n",
        "  try:\n",
        "    img_array = cv2.imread(os.path.join(test_dir,img),cv2.IMREAD_GRAYSCALE)\n",
        "    new_array = cv2.resize(img_array,(img_size, img_size))\n",
        "    if \"virus\" in i:\n",
        "      class_name = 1\n",
        "      data_test.append([new_array,class_name])\n",
        "    elif \"bacteria\" in i:\n",
        "      class_name = 2\n",
        "      data_test.append([new_array,class_name])\n",
        "    elif \"NORMAL\" in i:\n",
        "      class_name = 0\n",
        "      data_test.append([new_array,class_name])  \n",
        "    else:\n",
        "      pass\n",
        "  except Exception as e:\n",
        "    pass"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0aG-Aym9iRn6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "53946e03-f632-490e-f041-0bd5d3e12d42"
      },
      "source": [
        "len(data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4619"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6bBAJ5kwxrlH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "deb98c39-536c-40fd-e239-1a63baa5363f"
      },
      "source": [
        "len(data_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "555"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4ccMnefTp_fd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "random.shuffle(data)\n",
        "random.shuffle(data_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CxsLe0jiiXc-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "outputId": "66902389-90c7-48cc-96eb-66c1852eba85"
      },
      "source": [
        "plt.imshow(data[0][0])\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rn9akcuVqCk9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "97fa305d-2b31-4db4-b316-94037a4b858e"
      },
      "source": [
        "print(data[0][0].shape, data[0][1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(100, 100) 2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k0VyswvXqpyB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_train=[]\n",
        "y_train=[]\n",
        "for i in range(len(data)):\n",
        "    x_train.append(data[i][0])\n",
        "    y_train.append(data[i][1])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HO_nm1FuxzHt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_test=[]\n",
        "y_test=[]\n",
        "for i in range(len(data_test)):\n",
        "    x_test.append(data_test[i][0])\n",
        "    y_test.append(data_test[i][1])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "--QPmslwqp2v",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_train = np.array(x_train)\n",
        "y_train = np.array(y_train)\n",
        "x_test = np.array(x_test)\n",
        "y_test = np.array(y_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4XafBEaaqp6F",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "cf32a848-554c-43a8-9aa9-a99513021f2a"
      },
      "source": [
        "y_train"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2, 2, 2, ..., 2, 2, 2])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XMNilPhZqozZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_train = x_train.reshape(x_train.shape[0], img_size, img_size, 1)\n",
        "input_shape = (img_size, img_size, 1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "swBlxoj1yEz4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_test = x_test.reshape(x_test.shape[0], img_size, img_size, 1)\n",
        "input_shape = (img_size, img_size, 1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u9hf7kMEigKW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "aab58fdf-b424-4501-d9b8-aac7d4cc5d3c"
      },
      "source": [
        "x_train[0].shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(100, 100, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "No8lVH7vyLqw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "457ba5de-0b2a-40de-c8b1-33c68854ac90"
      },
      "source": [
        "x_test[0].shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(100, 100, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VA9ztp32stZr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_train = keras.utils.to_categorical(y_train, 3)\n",
        "y_test = keras.utils.to_categorical(y_test, 3)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hGVwQ4Dsu8Yn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = Sequential()\n",
        "model.add(Conv2D(32, kernel_size = (4, 4), strides = (1, 1),padding='same', activation='relu', input_shape=input_shape))\n",
        "model.add(MaxPooling2D(pool_size=(2,2), strides=(1,1))) \n",
        "\n",
        "model.add(Conv2D(64, kernel_size = (4, 4), strides = (1, 1),padding='same', activation='relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2,2), strides=(1,1)))\n",
        "model.add(Dropout(0.25))  \n",
        "    \n",
        "model.add(Conv2D(64, kernel_size = (4, 4), strides = (1, 1),padding='same', activation='relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2,2), strides=(1,1)))\n",
        "\n",
        "model.add(Conv2D(64, kernel_size = (4, 4), strides = (1, 1),padding='same', activation='relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2,2), strides=(1,1)))\n",
        "model.add(Dropout(0.25)) \n",
        "\n",
        "model.add(Conv2D(64, kernel_size = (4, 4), strides = (1, 1),padding='same', activation='relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2,2), strides=(1,1)))\n",
        "    \n",
        "model.add(Flatten())\n",
        "model.add(Dense(100, activation='relu'))\n",
        "model.add(Dropout(0.25))\n",
        "model.add(Dense(3, activation='softmax'))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cAZ5JR5ivGzx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.compile(optimizer='adam',loss='categorical_crossentropy',metrics=['accuracy'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NHoRz7JBztlS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 544
        },
        "outputId": "933c43a8-04c0-43d9-9d5b-f7d6f7ccf026"
      },
      "source": [
        "model.fit(x_train,y_train,batch_size=110,epochs=15)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/15\n",
            "4619/4619 [==============================] - 14s 3ms/step - loss: 143.8220 - accuracy: 0.4871\n",
            "Epoch 2/15\n",
            "4619/4619 [==============================] - 12s 3ms/step - loss: 0.9284 - accuracy: 0.5683\n",
            "Epoch 3/15\n",
            "4619/4619 [==============================] - 12s 3ms/step - loss: 0.8320 - accuracy: 0.6265\n",
            "Epoch 4/15\n",
            "4619/4619 [==============================] - 12s 3ms/step - loss: 0.7045 - accuracy: 0.6839\n",
            "Epoch 5/15\n",
            "4619/4619 [==============================] - 12s 3ms/step - loss: 0.6107 - accuracy: 0.7311\n",
            "Epoch 6/15\n",
            "4619/4619 [==============================] - 12s 3ms/step - loss: 0.5840 - accuracy: 0.7419\n",
            "Epoch 7/15\n",
            "4619/4619 [==============================] - 12s 3ms/step - loss: 0.5448 - accuracy: 0.7593\n",
            "Epoch 8/15\n",
            "4619/4619 [==============================] - 12s 3ms/step - loss: 0.5151 - accuracy: 0.7759\n",
            "Epoch 9/15\n",
            "4619/4619 [==============================] - 12s 3ms/step - loss: 0.4880 - accuracy: 0.7855\n",
            "Epoch 10/15\n",
            "4619/4619 [==============================] - 12s 3ms/step - loss: 0.4742 - accuracy: 0.7872\n",
            "Epoch 11/15\n",
            "4619/4619 [==============================] - 12s 3ms/step - loss: 0.4525 - accuracy: 0.8002\n",
            "Epoch 12/15\n",
            "4619/4619 [==============================] - 12s 3ms/step - loss: 0.4325 - accuracy: 0.8049\n",
            "Epoch 13/15\n",
            "4619/4619 [==============================] - 12s 3ms/step - loss: 0.4220 - accuracy: 0.8127\n",
            "Epoch 14/15\n",
            "4619/4619 [==============================] - 12s 3ms/step - loss: 0.3985 - accuracy: 0.8238\n",
            "Epoch 15/15\n",
            "4619/4619 [==============================] - 12s 3ms/step - loss: 0.3913 - accuracy: 0.8231\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.callbacks.History at 0x7f942a9a7668>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oYsU4gNWz4Or",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "164bcd45-6965-45f3-bbe1-43e0aa129bff"
      },
      "source": [
        "score = model.evaluate(x_test, y_test, verbose=0)\n",
        "print('Test loss:', score[0])\n",
        "print('Test accuracy:', score[1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test loss: 1.334493721712817\n",
            "Test accuracy: 0.7099099159240723\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3xMOtJxo1ZJ5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "508f5ada-eb76-4d99-edbb-812f119811cb"
      },
      "source": [
        "score = model.evaluate(x_train, y_train, verbose=0)\n",
        "print('Train accuracy:', score[1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train accuracy: 0.8594933748245239\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ojq4M-SZKyXx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}